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Anthony G. Barnston

Abstract

This paper presents new methods of estimating the bias and the resolution of radar and raingage area average rainfall measurements over a defined area when both devices are employed simultaneously.

The bias of raingage measurements for various rainfall amount ranges is estimated from published data, and the bias for radar measurement is then determined through comparison with the raingage recordings. The resolution estimations are carried out using error variance analysis on corresponding sets of gage and radar observations. The assumptions underlying this technique demand a uniform terrain for rainfall measurements, a large sample of cases, and, for one of the analysis options, a high correlation between radar and gage rainfall measurements.

The procedure is illustrated using the gage and radar rainfall data from the second phase of the Florida Area Cumulus Experiment (FACE-2). The gage sampling error variance estimations for various rainfall amount categories using an empirical radar-derived method are examined by comparison with those of published studies using alternate methods and are found to be in general agreement. The FACF,2 gage network is found to provide more highly resolved rainfall measurements than the WSR-57 radar in moderate or heavy rainfall, but the radar exhibits the superior resolution in light rainfall if the radar rainfall adjustment used in FACE is not carried out. The radar rainfall adjustment appears to reduce radar measurement bias quite effectively, but the resolution is generally not improved and is degraded in below-median rainfall amounts.

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Anthony G. Barnston

Abstract

In this study, the sources and strengths of statistical short-term climate predictability for local surface climate (temperature and precipitation) and 700-mb geopotential height in the Northern Hemisphere are explored at all times of the year at lead times of up to one year. Canonical correlation analysis is the linear statistical methodology employed. Predictor and predictand averaging periods of 1 and 3 months are used, with four consecutive predictor periods, followed by a lead time and then a single predictand period. Predictor fields are quasi-global sea surface temperature (SST), Northern Hemisphere 700-mb height, and prior values of the predictand field itself. Cross-validation is used to obtain, to first order, uninflated skill estimates.

Results reveal mainly modest statistical predictive skill except for certain fields, locations, and times of the year when predictability is far above chance expectation and good enough to be beneficial to appropriate users. The time of year when skills are generally highest is January through April. Global SST is the most skill-producing predictor field, perhaps because 1) the lower boundary condition is a more consistent influence on climate on timescales of 1 to 3 months than the atmosphere's internal dynamics, or 2) SST is the only field in this study that provides tropical information directly. Prediction is generally more skillful on the 3-month than 1-month timesale. The skill of the forecasts is often insensitive to the forecast lead time; that is, inserting 3, or sometimes 6 or more, months between the predictor and predictand periods causes little skill decrease from that of 1 month or less. This has favorable implications for long-lead forecasting.

Much of the higher skill occurs in association with fluctuations of the El Niño/Southern Oscillation (ENSO) and is found in midwinter through midspring in specific pockets of the Pacific and North American regions. Predictive skill for precipitation is also found in the same context but is lower than that for 700-mb height or temperature.

Warm season predictability, slightly lower than that of winter-spring and not clearly documented in earlier work, is related to episodes of like-signed SST anomalies in the tropical oceans throughout the world in the preceding months. There is an interdecadal component in the variability of these global SST conditions. Generalized positive (negative) 700-mb and surface temperature anomalies in middle to late summer (but fall in southern Europe), generally at subtropical latitudes throughout much of the Northern Hemisphere (but with some midlatitude continental protrusions), occur following episodes of uniformly positive (negative) SST anomalies in the tropical oceans throughout the world in the preceding winter through late spring. The occurrence of a mature warm (cold) ENSO extreme the previous winter may contribute to such a worldwide SST condition in the intervening spring season. In the United States, the effect is a general (monopole) anomalous warmth (coolness) from mid-July through August across much of the country.

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Anthony G. Barnston

Abstract

No abstract available.

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Anthony G. Barnston

Abstract

The correspondence among the following three forecast verification scores, based on forecasts and their associated observations, is described: 1) the correlation score, 2) the root-mean-square error (RMSE) score, and 3) the Heidke score (based on categorical matches between forecasts and observations). These relationships are provided to facilitate comparisons among studies of forecast skill that use these differing measures.

The Heidke score would be more informative, more “honest,” and easier to interpret at face value if the severity of categorical errors (i.e., one-class errors versus two-class errors, etc.) were included in the scoring formula. Without taking categorical error severity into account the meaning of Heidke scores depends heavily on the categorical definitions (particularly the number of categories), making intercomparison between Heidke and correlation (or RMSE) scores, or even among Heidke scores, quite difficult.

When categorical error severity is taken into account in the Heidke score, its correspondence with other verification measures more closely approximates that of more sophisticated scoring systems such as the experimental LEPS score.

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Michael K. Tippett
and
Anthony G. Barnston

Abstract

The cross-validated hindcast skills of various multimodel ensemble combination strategies are compared for probabilistic predictions of monthly SST anomalies in the ENSO-related Niño-3.4 region of the tropical Pacific Ocean. Forecast data from seven individual models of the Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) project are used, spanning the 22-yr period of 1980–2001. Skill of the probabilistic forecasts is measured using the ranked probability skill score and rate of return, the latter being an information theory–based measure. Although skill is generally low during boreal summer relative to other times of the year, the advantage of the model forecasts over simple historical frequencies is greatest at this time. Multimodel ensemble predictions, even those using simple combination methods, generally have higher skill than single model predictions, and this advantage is greater than that expected as a result of an increase in ensemble size. Overall, slightly better performance was obtained using combination methods based on individual model skill relative to methods based on the complete joint behavior of the models. This finding is attributed to the comparatively large expected sampling error in the estimation of the relations between model errors based on the short history. A practical conclusion is that, unless some models have grossly low skill relative to the others, and until the history is much longer than two to three decades, equal, independent, or constrained joint weighting are reasonable courses.

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Diriba Korecha
and
Anthony G. Barnston

Abstract

In much of Ethiopia, similar to the Sahelian countries to its west, rainfall from June to September contributes the majority of the annual total, and is crucial to Ethiopia’s water resource and agriculture operations. Drought-related disasters could be mitigated by warnings if skillful summer rainfall predictions were possible with sufficient lead time. This study examines the predictive potential for June–September rainfall in Ethiopia using mainly statistical approaches. The skill of a dynamical approach to predicting the El Niño–Southern Oscillation (ENSO), which impacts Ethiopian rainfall, is assessed. The study attempts to identify global and more regional processes affecting the large-scale summer climate patterns that govern rainfall anomalies. Multivariate statistical techniques are applied to diagnose and predict seasonal rainfall patterns using historical monthly mean global sea surface temperatures and other physically relevant predictor data. Monthly rainfall data come from a newly assembled dense network of stations from the National Meteorological Agency of Ethiopia. Results show that Ethiopia’s June–September rainy season is governed primarily by ENSO, and secondarily reinforced by more local climate indicators near Africa and the Atlantic and Indian Oceans. Rainfall anomaly patterns can be predicted with some skill within a short lead time of the summer season, based on emerging ENSO developments. The ENSO predictability barrier in the Northern Hemisphere spring poses a major challenge to providing seasonal rainfall forecasts two or more months in advance. Prospects for future breakthroughs in ENSO prediction are thus critical to future improvements to Ethiopia’s summer rainfall prediction.

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Amir Shabbar
and
Anthony G. Barnston

Abstract

An empirical system for forecasting 3-month mean surface temperature T and total precipitation P for Canada—canonical correlation analysis (CCA)—has been developed using the 1956–90 data period. The levels and sources of predictive skill have been estimated for all seasons at lead times of up to one year, using a cross-validation design. The predictor fields are quasi-global sea surface temperature (SST), Northern Hemisphere 500-mb geopotential height, and for T forecasts prior values of T itself. Four consecutive 3-month predictor periods are used to detect evolving as well as steady-state conditions in the predictor fields.

While forecast skills are modest for much of the year, winter and spring skills for T forecasts at a 3-month lead time are both highly statistically field significant and good enough to be beneficial to appropriate users. These forecasts average a 0.3–0.4 correlation skill nationwide and greater than 0.6 in the southeastern prairies. Forecast skill for P averages a lower but still statistically field significant 0.2 in winter with local maxima of greater than 0.5 along parts of southern Canada. A weak secondary seasonal maximum in T forecast skill is found in summer. CCA forecasts generally outperform persistence forecasts, and their skill declines only slowly as lead time is increased. Thus, useful forecasts can be made for certain seasons/regions of Canada several seasons in advance.

The CCA diagnostics indicate that the El Nin˜o/Southern Oscillation (ENSO) plays a dominant role in Canadian T anomalies in winter and spring, and P anomalies in winter. Warm SO (El Nin˜o) episodes tend to force positive winter and spring T anomalies in much of western and southern Canada, and suppressed P in roughly similar portions of the country. Below normal T tends to occur in northeastern Canada, and above normal P in the southeastern Northwest Territory, during warm SO episodes. Because of the linearity of CCA, opposite responses are implied for cold SO episodes. Another important skill source. for Canadian winter forecasts is associated with a long-term trend in global SST. Between the 1950s and the 1990s the high (low) latitude SST has tended to cool (warm). The Canadian winter T response has been a cooling from northern Quebec to northeastern Canada and warming in northwest Canada, while a trend toward greater (lighter) P in the northern (southern) prairies is noted. Knowledge of such trends can greatly aid in forecasting anomalies that are defined using normals for a period centered in the past.

In conclusion, statistically based long-lead forecasts of surface climate are shown to deliver useful skin in Canada. This approach also provides a skill benchmark against which the skill of dynamical models can be compared as they enter the forecasting arena.

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Anthony G. Barnston
and
Jack L. Thomas

Abstract

The agreement of radar with raingage rainfall measurements during the second phase of the Florida Area Cumulus Experiment (FACE-2) is examined. FACE-2 rainfall was measured in a 1.3 × 104 km2 target area using 111 nearly uniformly distributed gages at an average density of 117 km2 per gage and using a WSR-57 radar adjusted daily with a dense raingage network distributed over 500 km2. The radar versus gage agreement is studied in order to evaluate the accuracy of the unadjusted radar measurements and the effectiveness of the adjustment technique in improving the radar measurements. Implicit in the comparison is an assumption that the gage rainfalls are the relative standard of accuracy.

Before gage adjustment of the radar rainfalls, mean differences between radar and gage target area rainfalls are slightly positive (radar ∼ 1.10 × gage) on dry days but become considerably negative (radar ∼ 0.70 × gage) on wet days. Following gage adjustment, the mean agreement generally is much improved. However, the day-to-day variation of the differences is not diminished after the adjustment. This is attributed to misrepresentations of the gage-to-radar rainfall ratio in the dense raingage network which are caused by the spatial variation of that ratio within the overall target area.

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Anthony G. Barnston
and
Paul T. Schickedanz

Abstract

The synoptic and subsynoptic atmospheric processes that accompany statistically determined periods of irrigation-induced rainfall increases during the warm season in the Texas Panhandle are examined. Major results are as follows.

Irrigation appears to increase precipitation only when the synoptic condition provides low-level convergence and uplift, such that the additional moisture produced by irrigation (normally confined to the lowest 10–20 m of the atmosphere) is allowed to ascend to cloud base. Stationary fronts are the most favorable such synoptic condition because they fulfill the requirement for longer time durations than moving fronts or surface low pressure centers. The effect of irrigation is more noticeable during generally rainy periods because such periods often contain the types of significant rainfall events that provide sustained low-level convergence over the irrigated region. Because the mean storm track is closer to north Texas in June than in July and August, the irrigation-produced rainfall anomaly in June (which often is >20% in and somewhat downwind of the irrigation core) is the greatest of these three heavily irrigated months.

Irrigation appears to lower the daily surface maximum temperature by ∼2°C during dry, hot conditions and by ∼1°C on damp, cooler days. When combining the temperature anomalies with known increases in surface dewpoint, the lifted index is estimated to decrease by up to 1°C, slightly increasing the probability of convection, even in the absence of convergence.

Other possible mesoscale effects of irrigation are discussed.

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Anthony G. Barnston
and
Robert E. Livezey

Abstract

A recently discovered association between the 11-year solar cycle and the Northern Hemispheric low-frequency atmospheric circulation structure, which is most easily delectable when the two phases of the Quasi-biennial Oscillation (QBO) are considered individually, is described and subjected to global statistical significance tests.

Highly significant relationships are found during the January–February period. This is especially true for the west QBO phase, in which the solar flux is positively correlated with 700 mb heights and surface temperatures over central and northern Canada, and negatively correlated with heights in the western Atlantic along 30°N and with temperature in the southern and much of the eastern portions of the United States. The pattern of the flux-height correlation field resembles primarily the Tropical/Northern Hemisphere (TNH) long-wave circulation pattern and secondarily the North Atlantic Oscillation (NAO) pattern. For east QBO phase years a different structure is found, and for all years pooled a weaker but quite Characterizable pattern emerges.

January–February correlations are studied for sensitivity to lead time in the QBO phase definition and for shorter period means for the west QBO phase. The latter inquiry reveals a concentration of the west phase relationship during the latter half of January.

The climate of the October–November period also appears to participate, to a lesser but significant degree, in a solar–QBO relationship for west phase QBO years.

For the west QBO phase, the January–February solar flux versus 700 mb height (and United States–Canada surface temperature) correlation pattern contains sufficient amplitude and field significance to be exploited for operational forecasting purposes at the Climate Analysis Center. However, in the absence of a verifiable physical basis of the solar–QBO–atmosphere association, and because the 45 mb stratospheric winds were selected to characterize the QBO in an a posteriors manner, the relationships are accepted with caution and will be regularly reevaluated.

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